Inflammatory breast cancer microenvironment repertoire based on DNA methylation data deconvolution reveals actionable targets to enhance the treatment efficacy

Background Although the clinical signs of inflammatory breast cancer (IBC) resemble acute inflammation, the role played by infiltrating immune and stromal cells in this aggressive disease is uncharted. The tumor microenvironment (TME) presents molecular alterations, such as epimutations, prior to morphological abnormalities. These changes affect the distribution and the intricate communication between the TME components related to cancer prognosis and therapy response. Herein, we explored the global DNA methylation profile of IBC and surrounding tissues to estimate the microenvironment cellular composition and identify epigenetically dysregulated markers. Methods We used the HiTIMED algorithm to deconvolve the bulk DNA methylation data of 24 IBC and six surrounding non-tumoral tissues (SNT) (GSE238092) and determine their cellular composition. The prognostic relevance of cell types infiltrating IBC and their relationship with clinicopathological variables were investigated. CD34 (endothelial cell marker) and CD68 (macrophage marker) immunofluorescence staining was evaluated in an independent set of 17 IBC and 16 non-IBC samples. Results We found lower infiltration of endothelial, stromal, memory B, dendritic, and natural killer cells in IBC than in SNT samples. Higher endothelial cell (EC) and stromal cell content were related to better overall survival. EC proportions positively correlated with memory B and memory CD8+ T infiltration in IBC. Immune and EC markers exhibited distinct DNA methylation profiles between IBC and SNT samples, revealing hypermethylated regions mapped to six genes (CD40, CD34, EMCN, HLA-G, PDPN, and TEK). We identified significantly higher CD34 and CD68 protein expression in IBC compared to non-IBC. Conclusions Our findings underscored cell subsets that distinguished patients with better survival and dysregulated markers potentially actionable through combinations of immunotherapy and epigenetic drugs. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-024-05553-5.

4. Add in a schematic way also the experimental methods applied in each dataset and explain convincingly how the two methods are orthogonal for validation.Therefore, the findings of the immunofluorescence assays for CD34 and CD68 cross-validated the accuracy of HiTIMED's cell type predictions of these two cell populations relevant in the context of our study.We included these comments in the Discussion section (lines 520-523).
5. Lines 299-300 and stemming considerations: Survival Analysis, Univariate Effects and Cox Regression: a.Although a limited sample size precludes a meaningful multivariate analysis, it is essential to show univariate effects using Cox regression for all covariates.This analysis is needed to present the effect in terms of hazard ratio and could be supplemented with Wald's test instead of Cox regression.
Answer: We appreciate the comments and suggestions.As recommended, we applied the univariate analysis of clinicopathological variables and estimated cell fractions associated with patients' survival using Cox regression.Next, we used the Wald test to determine the p-values.The results are included in the following table (Table S4).Among the six variables (clinical stage, distant metastasis, M stage, N stage, EC, and stromal cell proportions) that significantly impacted IBC patients' overall survival according to the Kaplan-Meier analysis presented in the manuscript (Table 1), five were also significant according to the Cox regression analysis: clinical stage, distant metastasis, M stage, EC, and stromal cell content.Both univariate analyses showed similar results.We added these findings to the manuscript (lines 324-329), and Answer: In this new version of our manuscript, we performed survival analysis in the validation cohort comparing low and high CD34 and CD68 protein expression.The cutoff was based on the median value.
Following the reviewer's recommendation, the results of this analysis are found in the new supplementary table S5 (Additional file 1).We found no statistically significant difference in overall survival based on the expression levels of CD34 and CD68.We included a paragraph in the Results section addressing these comments (lines 418-423).
There are several potential explanations for this lack of statistical significance: (1) Sample size: The validation cohort was composed of 33 samples (17 IBC and 16 non-IBC), which can limit the statistical power to detect significant differences in survival.Considering the rarity of this tumor type, increasing the sample size was not possible to overcome this limitation.( 2

Answer:
We created a flowchart (Fig. S1, Additional file 2) depicting the experimental methods and analyses applied to each dataset of our study.The methods employed in the discovery and validation assays are orthogonal since they represent different experimental approaches to test the hypothesis generated.Hierarchical Tumor Immune Microenvironment Epigenetic Deconvolution (HiTIMED) is an algorithm used for high-resolution profiling of the tumor microenvironment by utilizing tumor-specific DNA methylation signatures to deconvolve cell types.It quantitatively assesses cell proportions but lacks spatial information.Remarkably, immunofluorescence can complement this gap by providing spatial context and confirming the presence and distribution of these specific cell types by targeting cell-type-specific markers.

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Cohort differences: In the discovery cohort, a deconvolution analysis covered a myriad of cells present in bulk samples from inflammatory breast cancer (IBC) patients.The results of this analysis were correlated with clinicopathological variables and highlighted two cellular components: endothelial and stromal cells.To validate the findings, we select two markers, CD34 and CD68, each representing the cell components under analysis in the discovery cohort.The lack of statistical significance on survival between low and high marker expression might reside in the cell population profile within the tumor microenvironment of IBCs.Other specific endothelial and stromal markers to be determined could account for significant findings and influence survival outcomes.To consistently prove the prognostic value of these markers in all patient populations, larger sample sizes of this rare breast cancer are mandatory.(3) Biological variability: The biological behavior of IBC can vary among patients.The expression levels of CD34 and CD68 might be influenced by other underlying genetic, molecular, or environmental factors that were not analyzed in this study.In summary, the lack of statistical significance in survival analysis for CD34 and CD68 in the validation dataset likely arises from a combination of factors, including sample size, biological differences, and marker specificity for this type of cancer.Future research should delve deeper into these factors and uncover new insights.c.The study population is heterogeneous regarding critical characteristics such as stage.This heterogeneity impacts the validity of survival analyses.To address this issue, consider two main options: Conduct separate analyses for non-metastatic and metastatic populations.Check the distribution of the endothelial cell (EC) clusters and stromal clusters (high versus low) based on clinical stage (IIIB+IIIC versus stage IV) using Fisher's exact test.Verify the correlation between EC clusters and stromal clusters to determine if they are independent perspectives of the same biological effect.Impact on Survival and Cluster Distribution: If significant differences are found in cluster distribution between non-metastatic and metastatic patients, it may affect the statements regarding survival impact.If Fisher's test shows significant differences in cluster distribution between non-metastatic and metastatic patients, refrain from making causal statements about the impact on survival.Instead, frame the results to indicate that metastatic patients have less stroma and suggest further studies to verify the impact on survival.Answer:As suggested, we verified the distribution of endothelial cell (EC) and stromal cell clusters (high versus low) according to the presence of metastasis (M0 versus M1) and clinical stage (IIIB+IIIC versus stage IV) using Fisher's exact test.The following stacked bar graphs depict the distribution of the clusters.Since we did not find statistically significant differences between non-metastatic and metastatic patients or stages III and IV (lines 319-323; Fig.S3, Additional file 5), we did not modify the manuscript regarding the EC and stromal cell content and the impact on IBC patients' survival.6. Validation of CD40 deregulation: The deregulation of CD40 identified in the discovery dataset was not explicitly validated in the second dataset.Include experiments to validate the CD40

Table S4
was included in Additional file 1.

Table S4 .
Univariate analysis of clinicopathological variables and estimated cell fractions using Cox regression.